Using Multi-Criteria Decision-Making Techniques for Industrial Robot Selection

SUMMARY & CONCLUSIONS The selection of industrial robots is crucial for enhancing manufacturing efficiency and overall output. This study evaluates the effectiveness of various Multi-Criteria Decision-Making (MCDM) techniques, including TOPSIS and VIKOR, in ranking robots based on a comprehensive set of criteria. A detailed dataset was utilized, incorporating factors such as availability, flexibility, performance indicators, and cost-effectiveness, to assess the competency of four MCDM methods. The analysis provides valuable insights into the comparative strengths and weaknesses of these MCDM techniques. By examining VIKOR and TOPSIS within the framework of industrial robot selection, the unique advantages and limitations of each method are identified. Additionally, the impact of different criteria weights on the rankings is explored, highlighting the sensitivity of MCDM methods to parameter adjustments. The findings offer practical knowledge for decision-makers facing challenges in robot selection, facilitating improved problem-solving and alignment of decision-making models with organizational goals and preferences. Understanding the performance characteristics of various MCDM techniques enhances institutions' ability to address industrial needs effectively. This comparative analysis is beneficial for professionals in industrial automation, robotics research, and policy development. It aids raw material manufacturers and other industries in achieving organizational objectives, improving efficiency, and enhancing competitiveness. The study underscores the importance of selecting the appropriate MCDM technique to optimize the evaluation and decision-making processes for industrial robots.